Question 1: What decisions drive waste in your business? Most businesses have large sources of waste. Consider the world of floral retailing. The average retail florist can sustain spoilage rates of more than 50% of their inventory. More than half of their flowers simply become refuse. So for innovators like UrbanStems and the Bouqs, the data that makes their businesses so disruptive is the data that enables them to eliminate that spoilage.

In the words of the Harvard Business School’s Ben Edelman, “waste makes for opportunity.” Whether it’s in industrial production, retailing, or legal investigations, figuring out your sources of wasted effort and resources should guide the way toward the right data. Whether it’s as simple as identifying predictions you know you make (how much inventory to stock) or whether it requires you to think about the decisions implicit in your business model (how a cab drives around the city at 10 PM), charting out the decisions will point you toward sources of waste.

Question 2: Which decisions could you automate to reduce waste? Once you have your decisions, the hypothetical becomes what you can actually change. Humans are wonderful at making certain types of decisions. When it comes to deciding which campaigns will elicit the most irrational reactions of other humans to branding and marketing materials, humans can be brilliant. These types of decisions should stay (for now) in the hands of people.

But when it comes to making simple, repetitive, operational decisions (like where to send a cab, how to price a product, or how many flowers to order to a floral shop), machines tend to be much better than people. And although many business models of the 20th century are predicated on human control of these decisions, today we can identify the data to automate more of these decisions than you’d imagine.

Amazon, for instance, is rumored to have eliminated almost all of its pricing team, pushing most pricing decisions toward algorithmic control. For most retailers this would be blasphemous. But if Amazon’s algorithm works, it would translate to far less spent on discounts, far less inventory piling up in warehouses, and better predictability of new product introductions — each of which would yield enormous competitive advantage.

Question 3: What data would you need to do so? Once you have an understanding of the waste in your legacy system and you’ve charted out the decisions that result in that waste, the last step is asking a simple question. If you could have any piece of information, however unbelievable, to make the perfect decision, what would it be?

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